Mines Saint-Étienne

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

This study aims at presenting the Ripple effect in supply chains. It develops different dimensions of the Ripple effect and summarises recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective. It structures and classifies existing research streams and applications areas of different quantitative methods to the Ripple effect analysis as well as identifying gaps in current research and delineating future research avenues. The analysis shows that different frameworks already exist implicitly for tackling the Ripple effect in the SC dynamics, control and disruption management domain. However, quantitative analysis tools are still rarely applied in praxis. We conclude that the Ripple effect can be the phenomenon that is able to consolidate research in SC disruption management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations. This may build the agenda for future research on SC dynamics, control, continuity and disruption management, making supply chains more robust, adaptable and profitable.

Wearable biosensors have emerged as an alternative evolutionary development in the field of healthcare technology due to their potential to change conventional medical diagnostics and health monitoring. However, a number of critical technological challenges including selectivity, stability of (bio)recognition, efficient sample handling, invasiveness, and mechanical compliance to increase user comfort must still be overcome to successfully bring devices closer to commercial applications. We introduce the integration of an electrochemical transistor and a tailor-made synthetic and biomimetic polymeric membrane, which acts as a molecular memory layer facilitating the stable and selective molecular recognition of the human stress hormone cortisol. The sensor and a laser-patterned microcapillary channel array are integrated in a wearable sweat diagnostics platform, providing accurate sweat acquisition and precise sample delivery to the sensor interface. The integrated devices were successfully used with both ex situ methods using skin-like microfluidics and on human subjects with on-body real-sample analysis using a wearable sensor assembly.

Electrolyte-gated organic transistors offer low bias operation facilitated by direct contact of the transistor channel with an electrolyte. Their operation mode is generally defined by the dimensionality of charge transport, where a field-effect transistor allows for electrostatic charge accumulation at the electrolyte/semiconductor interface, whereas an organic electrochemical transistor (OECT) facilitates penetration of ions into the bulk of the channel, considered a slow process, leading to volumetric doping and electronic transport. Conducting polymer OECTs allow for fast switching and high currents through incorporation of excess, hygroscopic ionic phases, but operate in depletion mode. Here, we show that the use of glycolated side chains on a thiophene backbone can result in accumulation mode OECTs with high currents, transconductance, and sharp subthreshold switching, while maintaining fast switching speeds. Compared with alkylated analogs of the same backbone, the triethylene glycol side chains shift the mode of operation of aqueous electrolyte-gated transistors from interfacial to bulk doping/transport and show complete and reversible electrochromism and high volumetric capacitance at low operating biases. We propose that the glycol side chains facilitate hydration and ion penetration, without compromising electronic mobility, and suggest that this synthetic approach can be used to guide the design of organic mixed conductors.

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.

If the best phase measurements are to be achieved, phase-stepping methods need algorithms that are 112 insensitive to the harmonic content of the sampled waveform and 122 insensitive to phase-shift miscalibration. A method is proposed that permits the derivation of algorithms that satisfy both requirements, up to any arbitrary order. It is based on a one-to-one correspondence between an algorithm and a polynomial. Simple rules are given to permit the generation of the polynomial that corresponds to the algorithm having the prescribed properties. These rules deal with the location and multiplicity of the roots of the polynomial. As a consequence, it can be calculated from the expansion of the products of monomials involving the roots. Novel algorithms are proposed, e.g., a six-sample one to eliminate the effects of the second harmonic and a 10-sample one to eliminate the effects of harmonics up to the fourth order. Finally, the general form of a self-calibrating algorithm that is insensitive to harmonics up to an arbitrary order is given.

A photolithographic process was used to integrate the conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) with parylene C, yielding highly conformable electrode arrays that were only four micrometers thick (shown here to conform to the midrib of a small leaf). The arrays were sufficiently self-supporting to allow in vivo evaluation in rats, yielding high-quality electrocorticography recordings.

This letter presents a method aimed at quantifying the dimensions of the heat-affected zone (HAZ), produced during nanosecond and femtosecond laser–matter interactions. According to this method, 0.1 μm thick Al samples were microdrilled and observed by a transmission electronic microscopy technique. The holes were produced at laser fluences above the ablation threshold in both nanosecond and femtosecond regimes (i.e., 5 and 2 J/cm2, respectively). The grain size in the samples was observed near the microholes. The main conclusion is that a 40 μm wide HAZ is induced by the nanosecond pulses, whereas the femtosecond regime does not produce any observable HAZ. It turns out that the width of the femtosecond HAZ is less than 2 μm, which is our observation limit.

A grain-based distinct element model featuring three-dimensional (3D) Voronoi tessellations (random poly-crystals) is proposed for simulation of crack damage development in brittle rocks. The grain boundaries in poly-crystal structure produced by Voronoi tessellations can represent flaws in intact rock and allow for numerical replication of crack damage progression through initiation and propagation of micro-fractures along grain boundaries. The Voronoi modelling scheme has been used widely in the past for brittle fracture simulation of rock materials. However the difficulty of generating 3D Voronoi models has limited its application to two-dimensional (2D) codes. The proposed approach is implemented in Neper, an open-source engine for generation of 3D Voronoi grains, to generate block geometry files that can be read directly into 3DEC. A series of Unconfined Compressive Strength (UCS) tests are simulated in 3DEC to verify the proposed methodology for 3D simulation of brittle fractures and to investigate the relationship between each micro-parameter and the model's macro-response. The possibility of numerical replication of the classical U-shape strength curve for anisotropic rocks is also investigated in numerical UCS tests by using complex-shaped (elongated) grains that are cemented to one another along their adjoining sides. A micro-parameter calibration procedure is established for 3D Voronoi models for accurate replication of the mechanical behaviour of isotropic and anisotropic (containing a fabric) rocks.

This paper surveys the vehicle routing problems met in cities for good distribution. It applies the following methodology. First, it gives an overview of the literature devoted to vehicle route optimization in cities. Then, it classifies and analyses urban logistic flows. As a result, it identifies the principal scientific challenges that need to be addressed: time-dependency, multi-level and multi-trip organization of the distribution, dynamic information. Finally, it focuses on each one of these challenges, analyses the main difficulties they imply and how they are treated in the literature.

The flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem, which has wide applications in the real world. The complexity and relevance of the FJSP have led to numerous research works on its modeling and resolution. This paper reviews some of the research of the past 30 years on the problem, by presenting and classifying the different criteria, constraints, configurations and solution approaches that have been considered. Recent emerging topics on complex shop scheduling, multi-criteria optimization and uncertain and dynamic environments are discussed. Finally, future research opportunities are proposed.

International audience

In a twist: A design strategy for molecule-based electronic materials using aromatic compounds with a nonplanar core is presented. A new class of hexabenzocoronene is put forth that has its core severely distorted out of planarity into a corrugated structure. When substituted with four alkoxy side chains, this material self-assembles into infinitely long columns through stacking (see picture) and acts as an active layer in field-effect transistors.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

This paper addresses the issue of designing experiments for a metamodel that needs to be accurate for a certain level of the response value. Such a situation is common in constrained optimization and reliability analysis. Here, we propose an adaptive strategy to build designs of experiments that is based on an explicit trade-off between reduction in global uncertainty and exploration of regions of interest. A modified version of the classical integrated mean square error criterion is used that weights the prediction variance with the expected proximity to the target level of response. The method is illustrated by two simple examples. It is shown that a substantial reduction in error can be achieved in the target regions with reasonable loss of global accuracy. The method is finally applied to a reliability analysis problem; it is found that the adaptive designs significantly outperform classical space-filling designs.

Abstract In many regions and at the planetary scale, human pressures on the environment exceed levels that natural systems can sustain. These pressures are caused by networks of human activities, which often extend across countries and continents due to global trade. This has led to an increasing requirement for methods that enable absolute environmental sustainability assessment (AESA) of anthropogenic systems and which have a basis in life cycle assessment (LCA). Such methods enable the comparison of environmental impacts of products, companies, nations, etc, with an assigned share of environmental carrying capacity for various impact categories. This study is the first systematic review of LCA-based AESA methods and their applications. After developing a framework for LCA-based AESA methods, we identified 45 relevant studies through an initial survey, database searches and citation analysis. We characterized these studies according to their intended application, impact categories, basis of carrying capacity estimates, spatial differentiation of environmental model and principles for assigning carrying capacity. We then characterized all method applications and synthesized their results. Based on this assessment, we present recommendations to practitioners on the selection and use of existing LCA-based AESA methods, as well as ways to perform assessments and communicate results to decision-makers. Furthermore, we identify future research priorities intended to extend coverage of all components of the proposed method framework, improve modeling and increase the applicability of methods.

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published bot h as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25–32] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.

Conjugated polymers have been increasingly considered for the design of conductive materials in the field of regenerative medicine. However, optimal scaffold properties addressing the complexity of the desired tissue still need to be developed. The focus of this study lies in the development and evaluation of a conductive scaffold for bone tissue engineering. In this study PEDOT:PSS scaffolds were designed and evaluated in vitro using MC3T3-E1 osteogenic precursor cells, and the cells were assessed for distinct differentiation stages and the expression of an osteogenic phenotype. Ice-templated PEDOT:PSS scaffolds presented high pore interconnectivity with a median pore diameter of 53.6 ± 5.9 µm and a total pore surface area of 7.72 ± 1.7 m2·g−1. The electrical conductivity, based on I-V curves, was measured to be 140 µS·cm−1 with a reduced, but stable conductivity of 6.1 µS·cm−1 after 28 days in cell culture media. MC3T3-E1 gene expression levels of ALPL, COL1A1 and RUNX2 were significantly enhanced after 4 weeks, in line with increased extracellular matrix mineralisation, and osteocalcin deposition. These results demonstrate that a porous material, based purely on PEDOT:PSS, is suitable as a scaffold for bone tissue engineering and thus represents a promising candidate for regenerative medicine. Tissue engineering approaches have been increasingly considered for the repair of non-union fractions, craniofacial reconstruction or large bone defect replacements. The design of complex biomaterials and successful engineering of 3-dimensional tissue constructs is of paramount importance to meet this clinical need. Conductive scaffolds, based on conjugated polymers, present interesting candidates to address the piezoelectric properties of bone tissue and to induce enhanced osteogenesis upon implantation. However, conductive scaffolds have not been investigated in vitro in great measure. To this end, we have developed a highly porous, electrically conductive scaffold based on PEDOT:PSS, and provide evidence that this purely synthetic material is a promising candidate for bone tissue engineering.

Nearly 300 million t of phosphogypsum (PG) are produced every year as a byproduct from phosphate fertilizer production worldwide. Approximately 58% of the PG are stacked, 28% are discharged in coastal waters and only 14% are further processed. This critical review provides an overview of the physical-chemical properties of PG produced from sedimentary and magmatic phosphate ore worldwide using various analytical tools. Results from more than 25 years of work on PG at École des Mines de Saint-Étienne are presented and critically discussed. In total PG samples from 67 industrial storage sites around the world and PG samples synthesized under different conditions in the laboratory have been considered. The low radioactivity present in PG (particularly PG produced from sedimentary phosphate rock) was identified as the main challenge to using PG as a raw material in construction. Water-soluble and volatile chemical compounds were identified as the main challenge to environmentally sound PG management. Although PG does (in most cases) not pose an immediate threat to the environment the authors recommend processing all PG instead of storing or disposing it, to eliminate potential long-term risks and utilize a relevant secondary resource.